4 posts tagged with "guide"

Following our prior post on wiring up an MCP server for our Django app — see How We Integrated Model Context Protocol (MCP) into Our Django App — we went back and revisited the architecture. "Too many tools" is still a huge problem for LLM productivity, which has continued into GPT5 and the latest Claude models so probably won't be solved toon. Cursor and Claude both work better when they have fewer tools to choose from, and our original setup exposed too many single-purpose GET tools. So we consolidated everything into a single, strongly-typed batch tool.

The result: one get tool, clearer schema, faster concurrent fetches, and less model confusion.

AI coding is evolving fast. With Claude Code support for custom commands, it's time to upgrade your workflows. One of the most powerful advanced agentic coding techniques is parallel development with Git worktrees—running multiple Claude agents simultaneously on different branches of your codebase using custom slash commands. Our adoption of this technique is inspired by the benchy repository from this video.

Let's break it down step-by-step so you can replicate this advanced workflow in your own repo using custom Claude Code commands.

MCPs work like magic. Internally we use them relentlessly inside Cursor, for Linear issues in particular. We decided to ship an MCP server with Agent Interviews mainly because it made sense for us to have it on our own product for testing, before we even provided it to our customers. We built it quickly and made choices-of-least-resistance so there may be better ways to do everything. This is why we wanted to share our experience, would love to hear your feedback.

So the headline is we decided to implement an MCP server, Model Context Protocol (MCP), in our Django application, built on top of our existing API endpoints, and get it working with Cursor and Claude 3.7.

Ready to harness the power of AI for conducting qualitative research or gathering feedback? Agent Interviews makes it easy to create and deploy AI research agents that conduct research interviews and generate insights in real time. This guide walks you through the essential steps to get your first research agent up and running quickly.

Goal: Launch a basic AI research agent focused on your research topics and insights, and start collecting qualitative research data instantly.